Method and system for shipping container loading and unloading estimation

ABSTRACT

A method at a computing device, the method including obtaining sensor data for a vehicle providing vibration frequency and magnitude; calculating an energy for each of a low frequency passband and a high frequency passband of a bandpass filter pair; finding an energy ratio based on the energy for the low frequency passband and the energy for the high frequency passband; applying weighting constants to each of the energy for the low frequency passband, the energy for the high frequency passband and the energy ratio to calculate a decision variable; and finding that the vehicle is unloaded if the decision variable is below a threshold and finding that the vehicle is loaded if the decision variable is above a threshold.

FIELD OF THE DISCLOSURE

The present disclosure relates to the transportation of goods, and inparticular relates to load detection for vehicles or for containers on atrailer chasses.

BACKGROUND

During the transportation of goods, determining whether a trailer isloaded is an important aspect of trailer asset management. With thetransportation of shipping containers using tractor trailers, it isimportant to understand whether a trailer, referred to herein as achassis, is loaded or not.

The information on vehicle loading may be beneficial to thetransportation company. In particular, a transportation company managinga fleet of vehicles needs to know which vehicles are loaded and whichvehicles are empty.

Typically, vehicle loading is determined by manual inspection, which isa cumbersome process that can be slow and inaccurate.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be better understood with reference to thedrawings, in which:

FIG. 1 is a side elevational view of a trailer chassis adapted toreceive shipping containers, the figure showing an example placement ofa sensor apparatus;

FIG. 2 is block diagram of an example sensor apparatus capable of beingused for embodiments of the present disclosure;

FIG. 3 is a block diagram showing an example architecture for the sensorapparatus of FIG. 2;

FIG. 4 is a block diagram showing a container and chassis acting on aspring and damper;

FIG. 5 is a plot showing the vibration magnitude as a function offrequency for a loaded and an unloaded chassis;

FIG. 6 is a plot showing eRatio distribution on raw data using twobandpass filters;

FIG. 7 is a plot showing a dependent variable shift from the plot ofFIG. 6;

FIG. 8 is a plot on a two dimensional plane showing a relationshipbetween eRatio and the energy of the lower bandpass filter;

FIG. 9 is a process diagram showing a process to train and then obtainloaded/unloaded decisions for a vehicle;

FIG. 10 is a dataflow diagram showing a process for training in which aserver calculates the eRatio, E_(L), and E_(H), and uses these tocalculate weighting constants;

FIG. 11 is a dataflow diagram showing a process for training in which asensor apparatus calculates the E_(L), and E_(H), and passes these to aserver to calculate eRatio and weighting constants;

FIG. 12 is a process diagram showing a process for training in which asensor apparatus calculates the eRatio, E_(L), and E_(H), and uses theseto calculate weighting constants;

FIG. 13 is a dataflow diagram showing a process for makingloaded/unloaded decisions in which a server calculates the eRatio,E_(L), and E_(H), and uses these and weighting constants to find adecision variable;

FIG. 14 is a dataflow diagram showing a process for makingloaded/unloaded decisions in which a sensor apparatus calculates theeRatio, E_(L), and E_(H), and uses these and weighting constants to finda decision variable;

FIG. 15A is a plot showing a histogram of the distribution of decisionvariables based on eRatio;

FIG. 15B is a plot showing a histogram of the distribution of decisionvariables based on minimum-mean squared error (MSE) solutions;

FIG. 16A is a plot showing a histogram of the distribution of decisionvariables based on eRatio;

FIG. 16B is a plot showing a histogram of the distribution of decisionvariables based on support-vector machine (SVM) solutions;

FIG. 17 is a plot on a two dimensional plane showing a relationshipbetween eRatio and the energy of the lower bandpass filter and havingMSE and SVM decision lines;

FIG. 18A is a plot showing a histogram of the distribution of decisionvariables based on eRatio;

FIG. 16B is a plot showing a histogram of the distribution of decisionvariables based on SVM using multiple bandpass filter pairs; and

FIG. 19 is a block diagram of an example computing device or servercapable of being used with the embodiments of the present disclosure.

DETAILED DESCRIPTION OF THE DRAWINGS

The present disclosure provides a method at a computing device, themethod comprising: obtaining sensor data for a vehicle providingvibration frequency and magnitude; calculating an energy for each of alow frequency passband and a high frequency passband of a bandpassfilter pair; finding an energy ratio based on the energy for the lowfrequency passband and the energy for the high frequency passband;applying weighting constants to each of the energy for the low frequencypassband, the energy for the high frequency passband and the energyratio to calculate a decision variable; and finding that the vehicle isunloaded if the decision variable is below a threshold and finding thatthe vehicle is loaded if the decision variable is above a threshold.

The present disclosure further provides a computing device comprising: aprocessor; and a communications subsystem, wherein the computing deviceis configured to: obtain sensor data for a vehicle providing vibrationfrequency and magnitude; calculate an energy for each of a low frequencypassband and a high frequency passband of a bandpass filter pair; findan energy ratio based on the energy for the low frequency passband andthe energy for the high frequency passband; apply weighting constants toeach of the energy for the low frequency passband, the energy for thehigh frequency passband and the energy ratio to calculate a decisionvariable; and find that the vehicle is unloaded if the decision variableis below a threshold and finding that the vehicle is loaded if thedecision variable is above a threshold.

The present disclosure further provides a computer readable medium forstoring instruction code, which, when executed by a processor on acomputing device cause the computing device to: obtain sensor data for avehicle providing vibration frequency and magnitude; calculate an energyfor each of a low frequency passband and a high frequency passband of abandpass filter pair; find an energy ratio based on the energy for thelow frequency passband and the energy for the high frequency passband;apply weighting constants to each of the energy for the low frequencypassband, the energy for the high frequency passband and the energyratio to calculate a decision variable; and find that the vehicle isunloaded if the decision variable is below a threshold and finding thatthe vehicle is loaded if the decision variable is above a threshold.

In accordance with the embodiments described below, cargo load detectionsystems utilizing a vertical accelerometer and/or strain gauges aredescribed.

In the embodiments described below, the load detection is performed on atrailer chassis which is connected to a tractor. However, in othercases, the measurements may be made on other shipping containers,including, but not limited to, railcars, trucks, automobiles, amongothers.

In order to perform load detection, a sensor apparatus may be affixed toa container, trailer, or other similar asset. Such sensor apparatus may,for example, be mounted inside of a chassis of a flatbed trailerconfigured to receive shipping containers. Reference is now made to FIG.1.

In the embodiment of FIG. 1, example truck trailer 110 is shown. In oneembodiment, the computing device may be mounted within the chassis ofthe trailer. For example, in one embodiment the computing device may bemounted above the rear wheels of the truck trailer 110. This is shown,for example, with sensor apparatus 112 in the embodiment of FIG. 1.

However, in other cases it may be beneficial to have a differentposition for the sensor apparatus. Further, in some embodiments it maybe useful to have a plurality of such sensor apparatuses within thetrailer 110.

The sensor apparatuses within trailer 110 may be used alone in someembodiments, or may be combined into sets of two or more sensorapparatuses and/or external sensors for load determination calculations.

In the embodiment of FIG. 1, trailer 110 is adapted to secure and carrya shipping container 120 thereon. Information on whether the shippingcontainer 120 is present or not would be useful to a transportationcompany.

Apparatus

One sensor apparatus for a vehicle, chassis, trailer, container, orother transportation asset is shown with regard to FIG. 2. The sensorapparatus of FIG. 2 is however merely an example and other sensingdevices could equally be used in accordance with the embodiments of thepresent disclosure.

Reference is now made to FIG. 2, which shows an example sensor apparatus210. Sensor apparatus 210 can be any computing device or network node.Such sensor apparatus or network node may include any type of electronicdevice, including but not limited to, mobile devices such as smartphonesor cellular telephones. Examples can further include fixed or mobiledevices, such as internet of things (IoT) devices, endpoints, homeautomation devices, medical equipment in hospital or home environments,inventory tracking devices, environmental monitoring devices, energymanagement devices, infrastructure management devices, vehicles ordevices for vehicles, fixed electronic devices, among others.

Sensor apparatus 210 comprises a processor 220 and at least onecommunications subsystem 230, where the processor 220 and communicationssubsystem 230 cooperate to perform the methods of the embodimentsdescribed herein. Communications subsystem 230 may, in some embodiments,comprise multiple subsystems, for example for different radiotechnologies.

Communications subsystem 230 allows sensor apparatus 210 to communicatewith other devices or network elements. Communications subsystem 230 mayuse one or more of a variety of communications types, including but notlimited to cellular, satellite, Bluetooth™, Bluetooth™ Low Energy,Wi-Fi, wireless local area network (WLAN), sub-giga hertz radios, nearfield communications (NFC), IEEE 802.15, wired connections such asEthernet or fiber, among other options.

As such, a communications subsystem 230 for wireless communications willtypically have one or more receivers and transmitters, as well asassociated components such as one or more antenna elements, localoscillators (LOs), and may include a processing module such as a digitalsignal processor (DSP) or System on Chip (SOC). As will be apparent tothose skilled in the field of communications, the particular design ofthe communication subsystem 230 will be dependent upon the communicationnetwork or communication technology on which the sensor apparatus isintended to operate.

Processor 220 generally controls the overall operation of the sensorapparatus 210 and is configured to execute programmable logic, which maybe stored, along with data, using memory 240. Memory 240 can be anytangible, non-transitory computer readable storage medium, includingDRAM, Flash, optical (e.g., CD, DVD, etc.), magnetic (e.g., tape), flashdrive, hard drive, or other memory known in the art.

Alternatively, or in addition to memory 240, sensor apparatus 210 mayaccess data or programmable logic from an external storage medium (notshown), for example through communications subsystem 230.

In the embodiment of FIG. 2, sensor apparatus 210 may utilize aplurality of sensors, which may either be part of sensor apparatus 210in some embodiments or may communicate with sensor apparatus 210 inother embodiments. For internal sensors, processor 220 may receive inputfrom a sensor subsystem 250.

Examples of sensors in the embodiment of FIG. 2 include a positioningsensor 251, a vibration sensor 252, a temperature sensor 253, one ormore image sensors/cameras 254, accelerometer 255, light sensors 256,gyroscopic sensors 257, a door sensor 258, a strain gauge 259, and othersensors 260. Other sensors may be any sensor that is capable of readingor obtaining data that may be useful for the sensor apparatus 210.However, the sensors shown in the embodiment of FIG. 2 are merelyexamples, and in other embodiments, different sensors or a subset ofsensors shown in FIG. 2 may be used. For example, in some cases the onlysensor may be an accelerometer or a strain gauge.

Further, accelerometer 255 would typically provide acceleration sensorsin three dimensions. Thus, accelerometer 255 would generally includethree individual accelerometers. The readings from each of the threeindividual accelerometers could be isolated.

Communications between the various elements of sensor apparatus 210 maybe through an internal bus 265 in one embodiment. However, other formsof communication are possible.

In the embodiment of FIG. 2, rather than an internal strain gauge sensor259, in some cases the strain gauge sensor may be external to the sensorapparatus and may be controlled by sensor apparatus 210. Strain gaugesensor 270 may, for example, be mounted together with sensor apparatus210 or may form part of sensor apparatus 210. A single or multiplestrain gauge sensors can be mounted on the longitudinal frame of thechassis to detect displacement when a container is loaded on thechassis. The strain gauge sensor may, in some embodiments, consist of amicroprocessor, a strain gauge, a battery, and a short-range technologyradio transmitter (Bluetooth, IEEE 802.15.4, or Wi-Fi). The sensor 270could be configured to measure vertical displacement, or both verticaland horizontal displacement relative to the chassis.

The sensor apparatus 210 may communicate with the strain gauge sensor270 or strain gauge 259 to query the strain gauge reading under atrigger condition. Examples of trigger conditions could be: on a regularwake-up schedule negotiated between the sensor(s) and the sensorapparatus unit; when the trailer is in motion; and/or when the trailerstops, among other options.

A strain gauge will typically be displaced when the container is loadedand could be calibrated to estimate the relative or absolute weight ofthe container.

Sensor apparatus 210 may be affixed to any fixed or portable platform.For example, sensor apparatus 210 may be affixed to shipping containersor truck trailers in one embodiment. In other embodiments, sensorapparatus 210 may be affixed to a chassis of a trailer, as for exampleshown in FIG. 1. In other cases, the sensor apparatus 210 may be affixedto any transportation asset for which load detection is needed,including self-propelled vehicles (e.g., automobiles, cars, trucks,buses, etc.), railed vehicles (e.g., trains and trams, etc.), and othertypes of vehicles including any combinations of any of the foregoing,whether currently existing or after arising, among others.

In other cases, sensor apparatus 210 may be part of a container thatcould be carried on or within a vehicle, for example container 120 fromFIG. 1. In accordance with the present disclosure, the term containermay include any sort of cargo or item transportation such as vehicles,intermodal containers, shipping bins, lock boxes, and other similarvessels.

Such a sensor apparatus 210 may be a power limited device. For example,sensor apparatus 210 could be a battery-operated device that can beaffixed to a shipping container or trailer in some embodiments. Otherlimited power sources could include any limited power supply, such as asmall generator or dynamo, a fuel cell, solar power, energy harvesting,among other options.

In other embodiments, sensor apparatus 210 may utilize external power,for example from the battery or power system of a tractor pulling thetrailer, via a wiring harness connected to a 7-pin plug, from a landpower source for example on a plugged-in recreational vehicle or from abuilding power supply, among other options. Thus, the sensor apparatus210 may also be connected to a power cord that receives its power from apower source.

External power may further allow for recharging of batteries to allowthe sensor apparatus 210 to then operate in a power limited mode again.Recharging methods may also include other power sources, such as, butnot limited to, solar, electromagnetic, acoustic or vibration charging.

The sensor apparatus from FIG. 2 may be used in a variety ofenvironments. One example environment in which the sensor apparatus maybe used is shown with regard to FIG. 3.

Referring to FIG. 3, three sensor apparatuses, namely sensor apparatus310, sensor apparatus 312, and sensor apparatus 314 are provided.

In the example of FIG. 3, sensor apparatus 310 may communicate through acellular base station 320 or through an access point 322. Access point322 may be any wireless communication access point.

Further, in some embodiments, sensor apparatus 310 could communicatethrough a wired access point such as Ethernet or fiber, among otheroptions.

The communication may then proceed over a wide area network such asInternet 330 and proceed to servers 340 or 342.

Similarly, sensor apparatus 312 and sensor apparatus 314 may communicatewith server 340 or server 342 through one or both of the base station320 or access point 322, among other options for such communication.

In other embodiments, any one of sensor apparatuses 310, 312 or 314 maycommunicate through satellite communication technology. This, forexample, may be useful if the sensor apparatus is travelling to areasthat are outside of cellular coverage or access point coverage.

In other embodiments, sensor apparatus 312 may be out of range of accesspoint 322, and may communicate with sensor apparatus 310 to allow sensorapparatus 310 to act as a relay for communications.

Communication between sensor apparatus 310 and server 340 may be onedirectional or bidirectional. Thus, in one embodiment sensor apparatus310 may provide information to server 340 but server 340 does notrespond. In other cases, server 340 may issue commands to sensorapparatus 310 but data may be stored internally on sensor apparatus 310until the sensor apparatus arrives at a particular location, possiblyduring a particular time window. In other cases, two-way communicationmay exist between sensor apparatus 310 and server 340.

A server, central server, processing service, endpoint, Uniform ResourceIdentifier (URI), Uniform Resource Locator (URL), back-end, and/orprocessing system may be used interchangeably in the descriptionsherein. The server functionality typically represents dataprocessing/reporting that are not closely tied to the location of sensorapparatuses 310, 312, 314, etc. For example, the server may be locatedessentially anywhere so long as it has network access to communicatewith sensor apparatuses 310, 312, 314, etc.

Server 340 may, for example, be a fleet management centralizedmonitoring station. In this case, server 340 may receive informationfrom a sensor apparatus associated with various trailers or cargocontainers, providing information such as the location of such cargocontainers, the temperature within such cargo containers, any unusualevents including sudden decelerations, temperature warnings when thetemperature is either too high or too low, cargo loading status for thetrailer, the mass of the trailer, among other data. The server 340 maycompile such information and store it for future reference.

Other examples of functionality for server 340 are possible.

In the embodiment of FIG. 3, servers 340 and 342 may further have accessto third-party information or information from other servers within thenetwork. For example, a data services provider 350 may provideinformation to server 340. Similarly, a data repository or database 360may also provide information to server 340.

For example, data services provider 350 may be a subscription-basedservice used by server 340 to obtain current road and weatherconditions, or may be an inventory control system in some cases.

Data repository or database 360 may for example provide information suchas image data associated with a particular location, aerial maps,detailed street maps, or other such information.

The types of information provided by data service provider 350 or thedata repository or database 360 is not limited to the above examples andthe information provided could be any data useful to server 340.

In some embodiments, information from data service provider 350 or thedata repository from database 360 can be provided to one or more ofsensor apparatuses 310, 312, or 314 for processing at those sensorapparatuses.

A sensor apparatus such as that described in FIGS. 2 and 3 above may beused to detect trailer loading of a container or trailer.

Calculating Trailer Loading

In accordance with some embodiments of the present disclosure, the rearsegment of a transportation vehicle or trailer can be modelled as asimple spring with a mass “m” representing the mass of the container andthe mass of the chassis, and a spring constant “k”, where k is thestiffness of the structure and suspension. Further, a damper which maybe dependent on the suspension of the chassis is represented with adamping constant “c”. For example, reference is now made to FIG. 4.

In the embodiment of FIG. 4, a container 410 has a mass “m_(c)”. Achassis 412 has a mass “m₁”. In the present disclosure, a mass “m” isthe combination of masses m₁and m_(c).

A spring 420 is shown having a spring constant “k”, which isrepresentative of the stiffness of the structure and suspension.

Further, the damping constant “c” is shown using a representation 422 ofthe suspension.

A displacement “h” shows the amount of motion of the chassis from therest position of the chassis towards the ground, for example when a bumpis encountered by the chassis while the vehicle is operating.

The equation describing the motion of the chassis in one-dimension,using a single spring-damper, can be expressed as provided in equation 1below:

$\begin{matrix}{{m\frac{d^{2}h}{dt^{2}}} = {{{- k}h} - {c\frac{dh}{dt}}}} & (1)\end{matrix}$

In equation 1, m is the mass of the system, h is the displacement of thechassis from a rest position, t is time, k is the spring constant and cis the damping constant.

Solving equation 1 for the frequency of vibration yields equation 2:

$\begin{matrix}{f = {{\frac{1}{2\pi}\sqrt{\frac{k}{m}\left( {1 - \frac{c^{2}}{4mk}} \right)}} = {\frac{1}{4\pi m}\sqrt{{4mk} - c^{2}}}}} & (2)\end{matrix}$

Based on equation 2, the frequency of vibration is inverselyproportional to the mass applied to the trailer. When the trailer isunder the same moving conditions, the vibration frequency of the trailerwill shift lower when a shipping container is loaded on the chassis.

If a truck, trailer or other similar asset is loaded within safeoperating limits, the springs on the axles should not experience anysignificant deformations, which may affect the frequency of vibration.

The amplitude of the oscillations depends on the forces of any impactsbetween the wheels in the ground, but do not affect the frequency ofvibration.

For example, reference is now made to FIG. 5, which shows an exampleplot of the vibration frequencies seen when a chassis is loaded orunloaded with a shipping container, respectively. As seen from FIG. 5,the peak frequency of the vibration shifts when the chassis is loaded.

Based on FIG. 5, by measuring the frequency of vibration as observed byan accelerometer in the vertical direction and/or by a strain gauge, adetection of whether the chassis or trailer is loaded can be made.

One method for using the different frequencies for a loaded and unloadedchassis may be to calculate the energy ratio (eRatio) for a comparisonbetween the lower frequencies and a combination of the lower and higherfrequencies. In particular, a pair of bandpass filters, one at alower-band and one at a higher-band, may be used. Such bandpass filtersmay be set around the typical loaded and unloaded frequencies to capturethe average energy around these frequencies.

After n tests, n log files of raw data from a sensor may be collected.For example, a log file may contain 1024 samples. However, this numberof samples is merely provided for illustration, and is not limiting.

The raw data from these files is then processed by a detector, whereeach of the lower-band bandpass filter (LBPF) and higher-band bandpassfilter (HBPF) generates n values of the output averaged energycorresponding to the n log files. For example, these output values maybe denoted by E_(L)(i) and E_(H)(i), where the subscript “L” and “H”indicate the LBPF and the HBPF respectively, and i=1, 2, . . . , n. Inthe detector, the energy ratio (eRatio) is further calculated as:

$\begin{matrix}{{R(i)} = \frac{E_{L}(i)}{{E_{L}(i)} + {E_{H}(i)}}} & (3)\end{matrix}$

Using equation 3 above, when the cut-off frequencies of both filters areset properly, ideally E_(H)(i) should be zero when the loading status(LS) is “ON”, resulting in R(i) being 1. Conversely, when the LS is“OFF”, ideally E_(L)(i) should be zero and in turn R(i) should also be0.

Under practical conditions, the energy ratio generally is found to bebetween zero and one using equation 3 above.

For example, reference is now made to FIG. 6, which shows a plot 610 ofeRatio values obtained in a real world test by analysing raw data with abandpass of the LBPF set to between 2 and 5.5 Hz and the HBPF set tobetween 5.5 and 9 Hz. As seen in FIG. 6, when the loading status is off,the eRatio is between approximately 0.1 and 0.5. When the loading statusis on, the eRatio falls between approximately 0.6 and 1.

In some cases, it may be easier to deal with a loading status of “off”corresponding to −1 and a loading status of “on” corresponding to 1. Inthis case, a dependent variable for the eRatio, R′(i), may be definedas:

$\begin{matrix}{{R^{\prime}(i)} = {{{2{R(i)}} - 1} = \frac{{E_{L}(i)} - {E_{H}(i)}}{{E_{L}(i)} + {E_{H}(i)}}}} & (4)\end{matrix}$

FIG. 6 may therefore be converted to plot 710 of FIG. 7 for R′(i),thereby making the cut off between the loading status of on and off as“0”. In particular, the decision rule in this case would be:

$\begin{matrix}{{L{S(i)}} = \left\{ \begin{matrix}{ON} & {{{if}\mspace{14mu}{R^{\prime}(i)}} \geq 0} \\{OFF} & {otherwise}\end{matrix} \right.} & (5)\end{matrix}$

The above ratios are based solely on the phenomenon that the vibratingfrequency (VF) of the chassis spring varies as a function of the loadingmass. In particular, heavier loading results in a lower VF. By comparingthe energy output from the LBPF and the HBPF using the eRatio, loadingstatus is detected. However, occasionally when R′(i) is close to 0 (i.e.when R(i) is close to 0.5) then the detection is not reliable and mayresult in an erroneous detection.

Vibration Magnitude

To improve loading status detection reliability, a vibration magnitude(VM) variation may also be considered for loading status. In particular,when a chassis is empty, and therefore lighter, a spring on the chassiswill typically vibrate more freely and hence with a higher VM.Conversely, when the chassis is loaded with a heavy container, thespring vibrates with a suppressed magnitude. In other words, the VM alsocarries certain information about loading status.

Therefore, in accordance with embodiments described below, loadingstatus detection may be based on both the energy ratio, R(i), and thevibration magnitude which is equivalently represented by the values ofE_(L)(i) and E_(H)(i).

Reference is now made to FIG. 8. The embodiment of FIG. 8 shows a plot810 showing an example relationship between the eRatio and E_(L). As canbe seen from the embodiment of FIG. 8, when both the eRatio and E_(L)are utilized, the two sets of data can be separated more easily. Inparticular, referring to the reading with an eRatio of 0.5, shown aspoint 820, merely using the eRatio would be inconclusive on whether thetrailer is loaded or unloaded. However, by using the E_(L), this pointis more easily differentiated as being unloaded.

In order to facilitate such separation, a method widely used in MachineLearning (ML) may be adopted, and a decision variable S(i) is definedfor the i-th test data. For instance, if the values of eRatio, E_(L) andE_(H) are to be used, equation 6 below may be defined as:S(i)=w ₀ +w ₁ R(i)+w ₂ E _(L)(i)+w ₃ E _(H)(i)  (6)

Equation 6 simply means that S(i) is a weighted linear combination ofR(i), E_(L)(i) and E_(H)(i), with proper weighting coefficients (WC) w₁,j=0,1,2 and 3. These WCs are to be optimized.

If only the eRatio and E_(L) are to be used, then w₃ can be set to zeroin equation 6 above.

In some cases, optimization may be achieved by training the detectorusing collected testing results with known loading status. Assumingenough raw data has been collected from n tests with known loadingstatus, the raw data for R(i), E_(L)(i) and E_(H)(i) can be derived.Then, using these variables and the known LS, optimum WCs can beobtained by training, as described below.

However, in some cases, the training may be performed for one type ofchassis and then the values for the WCs can be propagated to sensorapparatuses in similar chassis. In this case, training would only needto occur in a subset of the chassis.

After training is completed or once the WC values are received at thesensor apparatus, equation 6 above may then be used to calculate thevalue of S(i). Once the S(i) value is known, a loading status decisionmay be made based on equation 7 below.

$\begin{matrix}{{L{S(i)}} = \left\{ \begin{matrix}{ON} & {{{if}\mspace{14mu}{S(i)}} \geq 0} \\{OFF} & {otherwise}\end{matrix} \right.} & (7)\end{matrix}$

In equation 7 above, the value of “0” is an arbitrarily assigned valuedecided during training. As will be appreciated by those in the art,when using equation 6, the S(i) value can be found to be around anymidpoint by changing the WCs. Therefore, equation 7 is merely oneexample of a decision equation.

Training

Optimization of the weighting coefficients in equation 6 above throughtraining is a major topic of machine learning. Many well-known MLtechniques exist, including but not limited to logistic regression (LR)and neural networks (NN). In these methods, starting from a set ofrandomly selected initial values for WCs, a certain number of iterativecalculations may be performed. WCs are updated in each iteration, andeventually converge to the final values for WCs.

In the embodiments described herein for the application of cargo loaddetection, two solutions are described as examples for the obtaining ofoptimum WCs. In a first solution, a minimum-mean squared error (MSE)solution is provided. In a second solution, a support-vector machine(SVM) solution is provided. However, these solutions are provided onlyfor illustration and, in other cases, different optimization algorithmsmay be utilized to find the values of the WC variables.

Without a loss of generality, it is assumed that a solution to equation6 above is being found, using the values found for the eRatio, E_(L) andE_(H). In particular, raw data from n tests with a known loading statushave been collected, and from this raw data, R(i), E_(L)(i) and E_(H)(i)have been derived using a pair of the properly designed LBPF and HBPF,where i=1, 2, . . . , n. In machine learning these variables arereferred to as features, and the known loading status as the target.

Since there are n samples, where each sample has three featuresassociated with one target, for convenience the features may be denotedas x_(i1),x_(i2), and x_(i3) for R(i), E_(L)(i) and E_(H)(i)respectively. The target may be denoted as y_(i). In one example, thetarget y_(i)=1 if the known LS(i) is ON and the target y_(i)=−1 if theknown LS(i) is OFF.

MSE Solution

Using a minimum-mean squared error approach, since y_(i) is the targetfor each test, ideally the decision variable S(i)=y_(i). Thus fromequation 6 above, the equation for each value of i for the training setis:

$\begin{matrix}\left. \begin{matrix}{y_{1} = {w_{0} + {w_{1}x_{11}} + {w_{2}x_{12}} + {w_{3}x_{13}}}} \\{y_{2} = {w_{0} + {w_{1}x_{21}} + {w_{2}x_{22}} + w_{3^{x}23}}} \\\ldots \\{y_{n} = {w_{0} + {w_{1}x_{n1}} + {w_{2}x_{n2}} + {w_{3}x_{n3}}}}\end{matrix} \right\} & (8)\end{matrix}$

The purpose of training is to find a solution for the four unknown WCswhich satisfy (8) above under known values of y_(i) and x_(ij). This maybe done by utilizing matrix and vector notation as described below.

In particular, the X matrix may be defined as:

$\begin{matrix}{X = \begin{bmatrix}1 & x_{11} & x_{12} & x_{13} \\1 & x_{21} & x_{22} & x_{23} \\\; & \; & \ldots & \; \\1 & x_{n\; 1} & x_{n\; 2} & x_{n\; 3}\end{bmatrix}} & (9)\end{matrix}$

Further, a y matrix may be defined as:y=[y ₁ y ₂ . . . y _(n)]^(T)  (10)

And a w matrix may be defined as:w=[w ₀ w ₁ w ₂ w ₃]^(T)  (11)

In (10) and (11) above, the superscript T denotes a transpose. Using thematrices of (9), (10) and (11) above, the formulas from (8) can bewritten into a concise form as:y=Xw  (12)

Mathematically, when the number of samples is greater than the number offeatures plus 1 (in this case when n>4) then the equations in (8) haveno solution. However, there is a uniquely least squares solution for theWCs:w=X\y=(X ^(T) X)⁻¹ X ^(T) y  (13)

In equation 13 above, X\y reads ‘y is left-divided by X’. Further,equation 13 is valid if all columns of X are independent. Equation 13leads to the minimum mean-squared error between y and S=Xw.

In general, to accommodate m features, m≥1, the matrix X of (9) consistsof (m+1) columns with the first column consisting of all “1”, and wconsists of (m+1) WCs.

SVM Solution

A support-vector machine is a supervised binary classification algorithmof ML applicable to the embodiments of the present disclosure. SVM is arobust process to solve both simple and highly complex ML problems witha large number of features and a small number of samples.

After enough training iterations, SVM finds a linear decision surfacethat can separate the two classes with a maximum margin. In other words,a decision line in 2-D models or a decision hyperplane inmulti-dimension models may be found.

Mathematically, SVM gives a solution for the weighting coefficients wwhich has minimized magnitude ∥w∥ subject to the constraint of (14)below:YXw≥1  (14)

In (14) above, 1 is an all-one column vector, and Y is a diagonal matrixwhose diagonal elements are y_(j), j=1, 2, . . . , n, in order. Fastalgorithms have been developed to implement SVM. Two non-limitingexamples of such algorithms include John C. Platt, “Sequential MinimalOptimization: A Fast Algorithm for Training Support Vector Machines”,Technical Report MSR-TR-98-14, Apr. 21, 1998; and Stanford University,“The Simplified SMO Algorithm”, CS 229.

Multiple Pairs of Filters

In the above embodiments, a single pair of bandpass filters was used forthe filtering the raw data in order to make loaded/unloaded decisions.This assumes that the passbands represented by the cutoff frequencies ofthe two bandpass filters have been properly selected.

In practice, in some embodiments it may not be known exactly what orwhich frequency bands should be selected for each of the two bandpassfilters as the passband. In this case, in accordance with an alternativeembodiment of the present disclosure, multiple pairs of bandpass filtersmay be utilized.

In particular, a detector contains K pairs of LBPFs and HBPFs, where Kis greater than or equal to one. Each pair of filters is set withdifferent passbands. The raw data from a sensor may then be applied tothe K pairs of filters at the same time.

For example, Table 1 below illustrates one case in which K=3, and thepassbands for each of the pairs is shown.

TABLE 1 Example of 3 BPF pairs and passbands Passband (Hz) BPF pairindex LBPF HBPF 1 2.0~5.5 5.5~9.0 2 2.5~6.0 6.0~9.5 3 3.0~6.5  6.5~10.0

Therefore, as shown in Table 1, each pair of bandpass filters hasdifferent cutoff frequencies. For the i-th test, from the k-th BPF pair,k equals 1, 2 and 3, we can derive three variables: R_(k)(i),E_(k,L)(i), E_(k,H)(i), and in total there are nine variables/featuresavailable.

When there are three pairs of the bandpass filters, equation 6 above maybe rewritten as:S(i)=w ₀ +w ₁ R ₁(i)+w ₂ E _(1,L)(i)+w ₃ E _(1,H)(i)+w ₄ R ₂(i)+w ₅ E_(2,L)(i)+w ₆ E _(2,H)(i)+w ₇ R ₃(i)+w ₈ E _(3,L)(i)+w ₉ E_(3,H)(i)  (15)

Equation 15 above can be solved through various techniques, includingusing MSC or SVM as described above. Further, in some cases, ifE_(k,H)(i) is ignored then equation 15 may be rewritten as:S(i)=w ₀ +w ₁ R ₁(i)+w ₂ E _(1,L)(i)+w ₃ R ₂(i)+w ₄ E _(2,L)(i)+w ₅ R₃(i)+w ₆ E _(3,L)(i)  (16)

Again, equation 16 may be solved through various techniques, includingusing MSE or SVM as described above.

While the examples above provide for three pairs of bandpass filters,this is merely provided for illustration and more or less pairs ofbandpass filters could be utilized. In particular, if only one pair ofbandpass filters is utilized and then the solutions described above maybe used. However, in some cases two pairs of bandpass filters or four ormore pairs of bandpass filters may be used.

Process for Determining Loaded/Unloaded Status

In practice, to make determinations on the loaded/unloaded status of thevehicle, a sensor apparatus associated with the vehicle may measure thevibration frequency and magnitude. In some cases, this raw data may beprocessed at the sensor apparatus or other computing device associatedwith the vehicle. In other cases, this data may be provided to a serverto perform the processing.

For example, a high-level overview of a process for makingloaded/unloaded decisions is provided in FIG. 9. In the embodiment ofFIG. 9, the process starts at block 910 and proceeds to block 912 inwhich a determination is made on whether training is needed at theparticular vehicle. For example, training may not be needed if traininghas been previously performed and the various weighting coefficientshave been previously determined. These weighting coefficients may bestored at a sensor apparatus or computing device associated with avehicle, or may be stored at a server. Therefore, the determination atblock 912 may include finding whether the coefficients are stored at thesensor apparatus/computing device or whether a flag exists at the sensorapparatus/computing device to indicate that the coefficients are storedat a server, among other examples.

In other cases, training may not be needed if the chassis type issimilar to other chassis types in which training has already beenperformed. In this case, a server may pass weighting coefficients to thesensor apparatus or may indicate to the sensor apparatus that weightingcoefficients exist at the server.

In other cases, a server may issue a command to a sensor apparatus tostart a loaded/unloaded determination. In this case, the command mayinclude the weighting coefficients or an indication that the weightingcoefficients exist at the server. If this information is missing fromthe command, the sensor apparatus or other computing device associatedwith the vehicle may conclude training is required.

Other examples of where training may not be needed are also possible.

If training is needed, the process proceeds from block 912 to block 920in which training is performed. The performance of training is describedin more detail below with regard to FIGS. 10, 11, and 12.

Once the training has been performed, the process proceeds to block 930in which the weighting coefficients are determined. The determination ofthe weighting coefficients may result in that the weighting coefficientsbeing stored at a server or at the sensor apparatus.

After the training is completed, or if training is not needed at block912, and then sensor apparatus, along with a server in some cases, maymake loaded/unloading decisions, as shown at block 940, using theweighting coefficients that were previously determined or assigned tothe vehicle.

The process then proceeds to block 950 and ends.

Based on FIG. 9, raw data generated by a sensor associated with thevehicle is collected by a device installed on the chassis. Training maybe performed at either the sensor apparatus or by a remote server. Aloaded/unloaded decision for new data may then be made by either thedevice or the server.

If decisions and/or processing are performed by a server, a reliablecommunication link would typically exist between the sensor apparatusand the server.

With regard to training, training may occur completely at a server orother similar computing device, as for example illustrated in FIG. 10;at both the sensor apparatus and the server or computing device, as forexample illustrated in FIG. 11; or completely at the sensor apparatus ora computing device on a vehicle, as for example illustrated in FIG. 12.Other options or further distributed models are also possible.

Reference is now made to FIG. 10, which shows the primary aspects of thetraining occurring at a server 1012. In particular, a sensor apparatus1010 may in some cases receive a command 1020 from a server 1012 tostart a training process. However, command 1020 is optional and in othercases, the training may occur based on other factors such as input froma operator of the vehicle, a check within a sensor apparatus 1010 thatthe weighting coefficients are not known, among other options.

Once the training is started, either the sensor apparatus or the serverneeds to have a loading status of the the trailer or chassis input. Theinput may be based on various factors. For example, in some cases anoperator of the vehicle may input the loading status into the vehicle orsensor apparatus. This may occur based on a user interface on the sensorapparatus, a user interface on the vehicle itself, or a communicationinterface that the operator of the vehicle may access. For example, inone case the operator of the vehicle may be required to input theloading status into a text message which is sent to a server or to thesensor apparatus during the training period. In other cases, theoperator of the vehicle may be required to press a button or toggle aswitch to indicate whether the trailer is loaded or unloaded. Otheroptions for providing information to the sensor apparatus or serverabout the loading status of the vehicle are also possible.

In other cases, during the training period, a server may know whetherthe trailer is loaded or unloaded, for example based on a transportationschedule, and use such information to correlate with the sensor data.

In other cases, external inputs may be used to receive the loadingstatus on the vehicle. For example, a camera in a shipping yard mayprovide visual images of the vehicle as it leaves the shipping yard,thereby identifying whether the trailer is loaded or unloaded. Suchimage may verify if a container is loaded or unloaded, or may in somecases show the wheels closer to the chassis on a closed container if thetrailer is loaded, among other options.

In still other cases, the sensor apparatus may have a secondarymechanism for identifying whether the vehicle is unloaded or loaded,such as cameras, cargo loading detectors such as light beam or laserprojectors and detectors to sense whether a closed trailer includescargo, among other options.

Therefore, as illustrated in FIG. 10, one or both of sensor apparatus1010 or server 1012 may receive the loading status based on one or moreof the above techniques. If the sensor apparatus 1010 is receiving aloading status, this is shown at block 1030. If the server 1012 isreceiving the loading status, this is shown at block 1032 in theembodiment of FIG. 10.

Sensor apparatus 1010 may then collect raw data with regard to thefrequency and magnitude of vibration, as shown at block 1040. This maybe done, for example based on accelerometers or strain gauges, amongother options.

Once the raw data is collected, the raw data may be sent to server 1012,as shown by message 1042. If the loading status was received at block1030, message 1042 may further include the loading status.

In some cases, message 1042 includes a complete set of log informationto allow the server 1012 to determine the weighting coefficients. Inother cases, message 1042 may be periodically sent with partialinformation (for example one single log entry). In this case, the stepsat blocks 1020, 1030, 1032, 1040 and 1042 may be repeated numerous timesto allow server 1012 to compile enough data to determine the weightingcoefficients.

Once a complete set of training logs are received at server 1012, theserver may then calculate R_(k)(i), E_(k,L)(i), and E_(k,H)(i), as shownat block 1050. For example, E_(k,L)(i) and E_(k,H)(i) may be calculatedby applying one or more of the pairs of bandpass filters. These valuesmay then be used to calculate R_(k)(i).

Once R_(k)(i), E_(k,L)(i), and E_(k,H)(i) are calculated, the processproceeds to block 1052 in which the weighting coefficients may becalculated. The calculation of the weighting coefficients may use theMSE or SVM methods as described above, among other options.

Once the weighting coefficients are calculated, the process proceeds toblock 1054 in which the weighting coefficients may be stored at theserver, provided to the sensor apparatus, or both stored at the serverand provided to the sensor apparatus.

At this point the training is finished and the weighting coefficientsmay then be used for the loaded/unloaded determinations.

In some cases, more of the processing can be performed at the sensorapparatus or similar computing device associated with the vehicle.Reference is now made to FIG. 11.

In the embodiment of FIG. 11, sensor apparatus 1110 communicates withserver 1112.

As with the embodiment of FIG. 10, in the embodiment of Figure litheserver 1112 may signal to sensor apparatus 1110 to start training, asshown at message 1120. However, message 1120 is optional.

Further, similar to the embodiment of FIG. 10, the loading status may bereceived at one or both of the sensor apparatus 1110 or server 1112, asfor example shown with blocks 1130 and 1132.

The sensor apparatus 1110 then collects raw data, as shown by block1140. One a complete training log is compiled, in the embodiment of FIG.11 the process then proceeds to block 1142, in which the sensorapparatus or computing device associated with the vehicle calculatesE_(k,L)(i), and E_(k,H)(i) based on the one or more bandpass filterpairs.

Once calculated, the sensor apparatus 1110 may then provide E_(k,L)(i),and E_(k,H)(i) to server 1112 in message 1144. Further, if the loadingstatus was received at block 1130, the loading status may also beprovided in message 1144 to server 1112.

Server 1112, once having received all of the log information in one ormore messages 1144, may then calculate the R_(k)(i), as shown at block1150.

The process then proceeds to block 1152 in which the weightingcoefficients may be calculated, for example as provided above.

The process then proceeds to block 1154 in which the server 1112 maystore the weighting coefficients, send the weighting coefficients tosensor apparatus 1110, or both store the weighting coefficients and sendthe weighting coefficients to sensor apparatus 1110.

Thereafter, the weighting coefficients may be used to calculateloaded/unloaded status on the vehicle.

In still a further embodiment, the processing may be completely done atthe sensor apparatus. Reference is now made to FIG. 12.

In the embodiment of FIG. 12, the process starts at block 1210. Theprocess may start, for example, based on a trigger to start trainingprocess. Such trigger may be a command from a server, identificationthat weighting coefficients do not exist on the trailer, a manualindication that training is to occur, among other options.

The process then proceeds to block 1220, in which the loading status ofthe vehicle is input or received at the sensor apparatus. The loadingstatus may be provided to the sensor apparatus, for example, based onmanual input from an operator of the vehicle, from sensors on thevehicle or in the area surrounding the vehicle, from commands orinformation provided by a server or other computing device, among otheroptions.

The process then proceeds to block 1222 in which the raw data iscollected. Such raw data may include the readings from an accelerometerand/or a stress gauge, among other options.

From block 1222, the process proceeds to block 1230 in which that theR_(k)(i), E_(k,L)(i), and E_(k,H)(i) are calculated based on one or morebandpass filter pairs.

The process then proceeds to block 1240 in which the weightingcoefficients are calculated based on the techniques described above orbased on similar techniques.

The process then proceeds to block 1242 in which the weightingcoefficients are stored at the sensor apparatus, sent to the server, orboth stored at the sensor apparatus and sent to the server.

From block 1242, the process proceeds to block 1250 and ends.

The weighting coefficients calculated at block 1240 may thereafter beused for making loaded/unloaded decisions.

Once training is complete, the weighting coefficients may be used by oneor both of the sensor apparatus and server to calculate aloaded/unloaded status. Reference is now made to FIGS. 13 and 14.

In the embodiment of FIG. 13, the determination of whether the vehicleis loaded or unloaded is done at a server. In particular, a sensorapparatus 1310 communicates with a server 1312. In some cases, server1312 may provide a command 1320 to the sensor apparatus 1310 to make adetermination of whether the vehicle is loaded or unloaded. In othercases, a sensor apparatus 1310 may make periodic determinations ofloaded or unloaded status, or some other trigger at the sensor apparatus1310 may cause the start of the process to determine whether the traileris unloaded are loaded.

The process then proceeds to block 1330 in which raw data including thefrequency of vibration and the magnitude of vibration is obtained.

The raw data may then be provided to the server 1312, as shown bymessage 1332. In some cases, if the weighting coefficients are stored atthe sensor apparatus 1310, message 1332 may further include theweighting coefficients for the vehicle.

On receiving message 1332, the server 1312 may calculate R_(k)(i),E_(k,L)(i), and E_(k,H)(i) as provided above. This is done, for example,at block 1340 in the embodiment of FIG. 13.

The process then proceeds to block 1350 in which the weightingcoefficients which are either stored at server 1312 or received inmessage 1332 may be used to determine the loaded/unloaded status of thevehicle. For example, equations 6, 15 or 16 may be used to find decisionvariable S(i). Once found, the decision variable may then be input intoequation 7 to determine whether the vehicle is loaded or unloaded. Theequations may be solved, for example, by placing the variables into amatrix X from equation 9 above and then using w and X from equation 12.

In other cases, the processing may completely be done at the sensorapparatus. Reference is now made to FIG. 14.

The process of FIG. 14 starts at block 1410 and proceeds to block 1420in which a check is made to determine whether a trigger condition hasbeen met to perform a loaded/unloaded status check. As indicated above,such trigger may include a message from a server, a timer timing out, anexternal factor such as a geolocation being entered or exited, a manualinput, among other options.

Once the trigger is met, the process proceeds to block 1422 in which theraw data for the vehicle is collected. This may include sensor readingsfrom an accelerometer and/or a stress gauge, among other options.

From block 1422 the process proceeds to block 1430 in which theR_(k)(i), E_(k,L)(i), and E_(k,H)(i) are calculated as described above.

The process then proceeds to block 1450 in which the weightingcoefficients stored at the sensor apparatus (or received from a server)may be used to determine the loaded/unloaded status of the vehicle. Forexample, equations 6, 15 or 16 may be used to find decision variableS(i). Once found, the decision variable may then be input into equation7 to determine whether the vehicle is loaded or unloaded. The equationsmay be solved, for example, by placing the variables into a matrix Xfrom equation 9 above and then using w and X from equation 12.

In some cases, once the the loaded/unloaded status is determined, thesensor apparatus may report this status to the server (not shown).

The process then proceeds to block 1450 and ends.

The embodiments of FIGS. 9 to 14 therefore provide for training andsubsequent determination of loaded/unloaded status utilizing both of thefrequency of vibration and magnitude of vibration.

Practical Example 1

The embodiments above were tested in a real world environment. Inparticular, as shown in Table 2 in Appendix A below, relevant data withknown loading status is provided. In Table 2, the third column lists thethe known loading status, where 1 indicates loaded and −1 indicatesunloaded. This loading status constitutes the vector y from equation 12above. The fourth, fifth and sixth columns of Table 2 list the R(i),E_(L)(i), and E_(H)(i) respectively. These columns constitute the lastthree columns of the matrix X from equation 9 above.

As a simple example, consider a loading status detector utilizing twofeatures, namely the R(i) and E_(L)(i). Calculating using equation 13for MSE, weighting coefficients w_(MSE)=[−1.4742,3.1522,−0.1449]^(T) arefound. On the other hand, with SVM, the weighting coefficients arew_(SVM)=[−2.9758,6.3533,−0.3371]^(T).

To see the improvement compared to the detection scheme based on R′(i)only, the following from equation 17 was calculated:S=Xw  (17)

The resultant S(i) is listed in the 2^(nd)˜4^(th) columns of Table 4 inAppendix C.

The histogram distributions of the decision variables obtained from MSEvs. based on eRatio only are shown in FIGS. 15A and 15B. In particular,FIG. 15A shows the histogram distribution 1510 based on eRatio only.FIG. 15B shows the histogram distribution 1520 based on MSE.

The histogram distributions of the decision variables obtained from SVMvs. based on eRatio only are shown in FIGS. 16A and 16B. In particular,FIG. 16A shows the histogram distribution 1610 based on eRatio only.FIG. 16B shows the histogram distribution 1620 based on SVM.

In the examples of FIGS. 15A, 15B, 16A and 16B, it can be seen thedetection reliability with SVM is better than that with MSE, while MSEis better than the detector based on eRatio only.

FIG. 17 shows the decision lines for MSE and SVM on the 2D plane of FIG.8. Line 1710 in FIG. 17 is the SVM decision line to separate the twostatuses, and line 1720 is the MSE decision line. It is seen that theSVM line gives the maximum gap between the two sets of the data, as seenfor example with −1 line 1730 and +1 line 1740.

Practical Example 2

In another example, the outputs R(i) and E_(L)(i) from each of the threepairs of BPFs specified in Table 1 are used to train SVM. The trainingdata is provided in Table 3 of Appendix B below. Correspondingly, thenew decision variable is designated pursuant to equation 16 above.

After training with SVM, the weighting coefficients obtained arew=[−2.942, 3.433, 1.439, 2.098, 0.063, 0.407, −1.369]^(T).

The relevant data and results are listed in the last column of Table 4in Appendix C below.

FIGS. 18A and 18B show the distribution of the decision variable with 3BPF pairs vs. with that based on eRatio of a single pair of BPF. Inparticular, FIG. 18A shows the histogram distribution 1810 based oneRatio only. FIG. 18B shows the histogram distribution 1820 based on theweighted sum of six features. One benefit of the embodiment of FIG. 18Bis that there is no need to select the passbands of the BPFs, which nowis determined by training automatically.

Servers

A server such as servers 340, 342 or 350 may be any network node. Forexample, one simplified server that may perform the embodimentsdescribed above is provided with regards to FIG. 19.

In FIG. 19, server 1910 includes a processor 1920 and a communicationssubsystem 1930, where the processor 1920 and communications subsystem1930 cooperate to perform the methods of the embodiments describedherein.

The processor 1920 is configured to execute programmable logic, whichmay be stored, along with data, on the server 1910, and is shown in theexample of FIG. 19 as memory 1940. The memory 1940 can be any tangible,non-transitory computer readable storage medium, such as DRAM, Flash,optical (e.g., CD, DVD, etc.), magnetic (e.g., tape), flash drive, harddrive, or other memory known in the art. In one embodiment, processor1920 may also be implemented entirely in hardware and not require anystored program to execute logic functions.

Alternatively, or in addition to the memory 1940, the server 1910 mayaccess data or programmable logic from an external storage medium, forexample through the communications subsystem 1930.

The communications subsystem 1930 allows the server 1910 to communicatewith other devices or network elements.

Communications between the various elements of the server 1910 may bethrough an internal bus 1960 in one embodiment. However, other forms ofcommunication are possible.

The embodiments described herein are examples of structures, systems ormethods having elements corresponding to elements of the techniques ofthis application. This written description may enable those skilled inthe art to make and use embodiments having alternative elements thatlikewise correspond to the elements of the techniques of thisapplication. The intended scope of the techniques of this applicationthus includes other structures, systems or methods that do not differfrom the techniques of this application as described herein, and furtherincludes other structures, systems or methods with insubstantialdifferences from the techniques of this application as described herein.

While operations are depicted in the drawings in a particular order,this should not be understood as requiring that such operations beperformed in the particular order shown or in sequential order, or thatall illustrated operations be performed, to achieve desirable results.In certain circumstances, multitasking and parallel processing may beemployed. Moreover, the separation of various system components in theimplementation descried above should not be understood as requiring suchseparation in all implementations, and it should be understood that thedescribed program components and systems can generally be integratedtogether in a single software product or packaged into multiple softwareproducts. In some cases, functions may be performed entirely in hardwareand such a solution may be the functional equivalent of a softwaresolution.

Also, techniques, systems, subsystems, and methods described andillustrated in the various implementations as discrete or separate maybe combined or integrated with other systems, modules, techniques, ormethods. Other items shown or discussed as coupled or directly coupledor communicating with each other may be indirectly coupled orcommunicating through some interface, device, or intermediate component,whether electrically, mechanically, or otherwise. Other examples ofchanges, substitutions, and alterations are ascertainable by one skilledin the art and may be made.

While the above detailed description has shown, described, and pointedout the fundamental novel features of the disclosure as applied tovarious implementations, it will be understood that various omissions,substitutions, and changes in the form and details of the systemillustrated may be made by those skilled in the art. In addition, theorder of method steps is not implied by the order they appear in theclaims.

When messages are sent to/from an electronic device, such operations maynot be immediate or from the server directly. They may be synchronouslyor asynchronously delivered, from a server or other computing systeminfrastructure supporting the devices/methods/systems described herein.The foregoing steps may include, in whole or in part,synchronous/asynchronous communications to/from thedevice/infrastructure. Moreover, communication from the electronicdevice may be to one or more endpoints on a network. These endpoints maybe serviced by a server, a distributed computing system, a streamprocessor, etc. Content Delivery Networks (CDNs) may also providecommunication to an electronic device. For example, rather than atypical server response, the server may also provision or indicate datafor a content delivery network (CDN) to await download by the electronicdevice at a later time, such as a subsequent activity of electronicdevice. Thus, data may be sent directly from the server, or otherinfrastructure, such as a distributed infrastructure, or a CDN, as partof or separate from the system.

Typically, storage mediums can include any or some combination of thefollowing: a semiconductor memory device such as a dynamic or staticrandom access memory (a DRAM or SRAM), an erasable and programmableread-only memory (EPROM), an electrically erasable and programmableread-only memory (EEPROM) and flash memory; a magnetic disk such as afixed, floppy and removable disk; another magnetic medium includingtape; an optical medium such as a compact disk (CD) or a digital videodisk (DVD); or another type of storage device. Note that theinstructions discussed above can be provided on one computer-readable ormachine-readable storage medium, or alternatively, can be provided onmultiple computer-readable or machine-readable storage media distributedin a large system having possibly plural nodes. Such computer-readableor machine-readable storage medium or media is (are) considered to bepart of an article (or article of manufacture). An article or article ofmanufacture can refer to any manufactured single component or multiplecomponents. The storage medium or media can be located either in themachine running the machine-readable instructions, or located at aremote site from which machine-readable instructions can be downloadedover a network for execution.

In the foregoing description, numerous details are set forth to providean understanding of the subject disclosed herein. However,implementations may be practiced without some of these details. Otherimplementations may include modifications and variations from thedetails discussed above. It is intended that the appended claims coversuch modifications and variations.

APPENDIX A

TABLE 2 Training Data for a Single BPF Pair ((2~5.5) + (5.5-9) Hz) FileName Log # Known LS eRatio EL EH Asset208449- 48 1 0.7497 0.9388 0.3134FLX1970A0 49 1 0.8323 1.9054 0.3839 31718009AE 50 1 0.8292 3.7833 0.779451 1 0.9065 3.5198 0.3632 52 1 0.9156 1.1674 0.1076 53 1 0.8481 2.63900.4727 54 1 0.9209 1.8088 0.1553 55 1 0.9275 2.9306 0.2291 56 1 0.85160.8201 0.1430 57 1 0.8445 2.4012 0.4422 58 1 0.9326 1.9563 0.1414 59 10.84 1.0406 0.1982 60 1 0.9532 4.8004 0.2357 61 1 0.8588 1.8466 0.303762 −1 0.5 4.8886 4.8877 63 −1 0.3701 2.0986 3.5713 64 −1 0.3515 0.81741.5082 65 −1 0.2396 0.9107 2.8908 104 1 0.7066 0.5319 0.2209Asset433027- 105 1 0.6669 0.3782 0.1889 FLX1970A03 106 1 0.7823 0.65740.1829 1618006AB 107 1 0.7518 0.6189 0.2043 108 1 0.8698 0.8918 0.1334109 1 0.6544 0.3847 0.2032 110 1 0.8024 0.6423 0.1581 111 1 0.69080.4550 0.2036 112 1 0.874 0.7852 0.1132 113 1 0.8982 1.8372 0.2082 114 10.764 0.4767 0.1472 115 1 0.6952 0.4473 0.1961 116 1 0.8866 0.54300.0694 117 −1 0.2347 1.0660 3.4767 118 −1 0.1597 0.5663 2.9787 119 −10.1957 0.8602 3.5348 120 −1 0.2166 1.8590 6.7222 121 −1 0.2352 0.26550.8635 122 −1 0.1991 0.3051 1.2268 123 −1 0.3036 1.4941 3.4272 124 −10.3245 0.8070 1.6801 125 −1 0.2551 0.8932 2.6082 126 −1 0.1617 0.72053.7341 127 −1 0.2269 2.8057 9.5576 128 −1 0.2801 0.0594 0.1527 75 10.8684 0.5964 0.0904 76 1 0.9067 0.6051 0.0623 Asset454009- 77 1 0.86460.9494 0.1487 FLX1970A03 78 1 0.7971 0.5246 0.1335 1618006AE 79 1 0.84360.8036 0.1490 80 1 0.8774 0.9959 0.1391 81 1 0.8702 1.5127 0.2256 82 10.8635 0.9256 0.1463 83 1 0.7573 0.3962 0.1270 84 1 0.8776 0.3224 0.045087 1 0.8271 1.1283 0.2358 88 1 0.789 0.5321 0.1423 89 1 0.829 0.47140.0972 90 1 0.7964 0.7724 0.1974 91 1 0.8138 1.0169 0.2327 92 −1 0.13231.0562 6.9282 93 −1 0.1341 0.1930 1.2462 94 −1 0.0969 0.4666 4.3480 95−1 0.165 1.0152 5.1389 96 −1 0.1491 0.4239 2.4198 97 −1 0.1108 0.37693.0258 98 −1 0.103 0.7284 6.3433 99 −1 0.215 0.7899 2.8840 100 −1 0.1840.6384 2.8317 101 −1 0.1352 0.1593 1.0190 102 −1 0.1458 0.3729 2.1841103 −1 0.1248 0.8750 6.1380 104 −1 0.1817 0.1689 0.7604

APPENDIX B

TABLE 3 Training Data for Three BPF Pairs 1^(st) BPF Pair 2^(nd) BPFPair 3^(rd) BPF Pair Known (2~5.5) + (5.5~9) Hz (2.5~6) + (6~9.5) Hz(3~6.5) + (6.5 + 10) Hz LS eRatio E_(L) E_(H) eRatio E_(L) E_(H) eRatioE_(L) E_(H) 1 0.7497 0.9388 0.3134 0.7800 0.9928 0.2800 0.7939 1.00140.2600 1 0.8323 1.9054 0.3839 0.8876 2.0123 0.2548 0.9176 2.0483 0.18401 0.8292 3.7833 0.7794 0.8717 3.9975 0.5884 0.8877 4.0027 0.5066 10.9065 3.5198 0.3632 0.9282 3.6239 0.2803 0.9337 3.6114 0.2566 1 0.91561.1674 0.1076 0.9296 1.1870 0.0899 0.9343 1.1705 0.0823 1 0.8481 2.63900.4727 0.8556 2.7315 0.4610 0.8472 2.7391 0.4942 1 0.9209 1.8088 0.15530.9543 1.8694 0.0896 0.9665 1.8593 0.0645 1 0.9275 2.9306 0.2291 0.95062.9937 0.1556 0.9620 2.9784 0.1177 1 0.8516 0.8201 0.1430 0.8641 0.81490.1282 0.8682 0.7835 0.1189 1 0.8445 2.4012 0.4422 0.8633 2.3682 0.37500.8699 2.2399 0.3351 1 0.9326 1.9563 0.1414 0.9511 1.9997 0.1027 0.95621.9865 0.0910 1 0.84 1.0406 0.1982 0.8591 1.0632 0.1744 0.8717 1.06070.1561 1 0.9532 4.8004 0.2357 0.9742 4.9455 0.1308 0.9803 4.9241 0.09881 0.8588 1.8466 0.3037 0.8801 1.8719 0.2549 0.8910 1.8419 0.2254 −1 0.54.8886 4.8877 0.6621 6.3487 3.2402 0.7686 7.3643 2.2175 −1 0.3701 2.09863.5713 0.5331 2.9479 2.5821 0.6850 3.7629 1.7305 −1 0.3515 0.8174 1.50820.4623 1.0888 1.2665 0.5748 1.3659 1.0104 −1 0.2396 0.9107 2.8908 0.33931.3285 2.5870 0.4551 1.7991 2.1537 1 0.7066 0.5319 0.2209 0.7461 0.57280.1950 0.7525 0.5729 0.1884 1 0.6669 0.3782 0.1889 0.6781 0.3909 0.18560.6704 0.3932 0.1933 1 0.7823 0.6574 0.1829 0.8211 0.6977 0.1520 0.84120.7043 0.1329 1 0.7518 0.6189 0.2043 0.7763 0.6448 0.1858 0.7881 0.65570.1763 1 0.8698 0.8918 0.1334 0.9131 0.9507 0.0905 0.9304 0.9449 0.07071 0.6544 0.3847 0.2032 0.6935 0.4191 0.1852 0.7052 0.4277 0.1788 10.8024 0.6423 0.1581 0.8400 0.6791 0.1294 0.8516 0.6839 0.1192 1 0.69080.4550 0.2036 0.7153 0.4802 0.1911 0.7265 0.4830 0.1819 1 0.874 0.78520.1132 0.9255 0.8441 0.0679 0.9397 0.8398 0.0539 1 0.8982 1.8372 0.20820.9154 1.8231 0.1686 0.9285 1.7585 0.1355 1 0.764 0.4767 0.1472 0.78920.4931 0.1317 0.7903 0.4948 0.1313 1 0.6952 0.4473 0.1961 0.7298 0.47550.1760 0.7449 0.4772 0.1635 1 0.8866 0.5430 0.0694 0.9244 0.5592 0.04570.9419 0.5513 0.0340 −1 0.2347 1.0660 3.4767 0.3299 1.5389 3.1257 0.43462.0766 2.7021 −1 0.1597 0.5663 2.9787 0.2374 0.8680 2.7890 0.3280 1.23932.5391 −1 0.1957 0.8602 3.5348 0.3465 1.4911 2.8125 0.5756 2.3649 1.7434−1 0.2166 1.8590 6.7222 0.3367 2.8890 5.6921 0.4463 3.9055 4.8457 −10.2352 0.2655 0.8635 0.3526 0.4008 0.7360 0.4930 0.5584 0.5742 −1 0.19910.3051 1.2268 0.2869 0.4563 1.1340 0.3934 0.6407 0.9879 −1 0.3036 1.49413.4272 0.4733 2.2828 2.5406 0.6016 2.9756 1.9705 −1 0.3245 0.8070 1.68010.4840 1.1730 1.2506 0.6303 1.5315 0.8983 −1 0.2551 0.8932 2.6082 0.41551.4230 2.0015 0.6151 2.0447 1.2792 −1 0.1617 0.7205 3.7341 0.2522 1.15663.4293 0.3754 1.7507 2.9126 −1 0.2269 2.8057 9.5576 0.3459 4.2550 8.04680.4732 5.8798 6.5464 −1 0.2801 0.0594 0.1527 0.3907 0.0833 0.1299 0.51150.1092 0.1043 1 0.8684 0.5964 0.0904 0.9286 0.6433 0.0495 0.9534 0.63860.0312 1 0.9067 0.6051 0.0623 0.9425 0.6334 0.0387 0.9545 0.6245 0.02981 0.8646 0.9494 0.1487 0.9000 0.9614 0.1068 0.9133 0.9191 0.0873 10.7971 0.5246 0.1335 0.8323 0.5491 0.1107 0.8505 0.5552 0.0976 1 0.84360.8036 0.1490 0.8761 0.8381 0.1185 0.8959 0.8378 0.0974 1 0.8774 0.99590.1391 0.9327 1.0620 0.0767 0.9595 1.0669 0.0451 1 0.8702 1.5127 0.22560.9100 1.5562 0.1540 0.9311 1.5500 0.1146 1 0.8635 0.9256 0.1463 0.89370.9565 0.1137 0.9071 0.9559 0.0979 1 0.7573 0.3962 0.1270 0.8504 0.45020.0792 0.8829 0.4692 0.0622 1 0.8776 0.3224 0.0450 0.9349 0.3484 0.02430.9539 0.3499 0.0169 1 0.8271 1.1283 0.2358 0.8671 1.1516 0.1765 0.88721.1279 0.1435 1 0.789 0.5321 0.1423 0.8441 0.5686 0.1050 0.8639 0.56700.0893 1 0.829 0.4714 0.0972 0.8843 0.5111 0.0669 0.9081 0.5220 0.0528 10.7964 0.7724 0.1974 0.8317 0.8014 0.1621 0.8451 0.8001 0.1467 1 0.81381.0169 0.2327 0.8923 1.0953 0.1322 0.9313 1.1332 0.0836 −1 0.1323 1.05626.9282 0.2005 1.6771 6.6876 0.3410 2.7983 5.4068 −1 0.1341 0.1930 1.24620.1934 0.2901 1.2099 0.2945 0.4477 1.0726 −1 0.0969 0.4666 4.3480 0.15360.7755 4.2740 0.2652 1.3470 3.7315 −1 0.165 1.0152 5.1389 0.2302 1.47754.9418 0.3303 2.1534 4.3653 −1 0.1491 0.4239 2.4198 0.2014 0.6012 2.38380.2922 0.8909 2.1586 −1 0.1108 0.3769 3.0258 0.1700 0.6062 2.9608 0.27901.0039 2.5938 −1 0.103 0.7284 6.3433 0.1393 1.0309 6.3677 0.2096 1.60906.0692 −1 0.215 0.7899 2.8840 0.2960 1.1277 2.6828 0.4345 1.6112 2.0973−1 0.184 0.6384 2.8317 0.2500 0.9085 2.7259 0.3516 1.3108 2.4178 −10.1352 0.1593 1.0190 0.1930 0.2420 1.0121 0.3060 0.3845 0.8719 −1 0.14580.3729 2.1841 0.2127 0.5712 2.1146 0.3030 0.8456 1.9452 −1 0.1248 0.87506.1380 0.2038 1.4901 5.8218 0.3715 2.6183 4.4290 −1 0.1817 0.1689 0.76040.2556 0.2468 0.7186 0.3704 0.3599 0.6117

APPENDIX C

TABLE 4 Training Results using using eRatio and E_(L) of the singleusing eRatio and E_(L) of known eRatio BPF pair the 3 BPF pairs LS R′(i) S_(MSE)(i) S_(SVM)(i) S_(SVM)(i) 1 0.4994 0.7538 1.4708 1.6343 10.6646 0.8741 1.6698 2.2164 1 0.6584 0.5922 1.0171 2.3132 1 0.81300.8741 1.5970 2.8485 1 0.8312 1.2438 2.4477 2.6845 1 0.6962 0.81761.5229 2.3305 1 0.8418 1.1675 2.2652 2.7910 1 0.8550 1.0257 1.92902.9578 1 0.7032 1.0923 2.1582 2.3069 1 0.6890 0.8408 1.5802 2.6616 10.8652 1.1831 2.2898 2.8667 1 0.6800 1.0238 2.0102 2.2116 1 0.90640.8358 1.4621 3.2540 1 0.7176 0.9662 1.8579 2.4698 −1 0 −0.6061 −1.4470−2.1664 −1 −0.2598 −0.6113 −1.3318 −2.2179 −1 −0.2970 −0.4843 −1.0182−1.1558 −1 −0.5208 −0.8507 −1.7605 −2.2901 1 0.4132 0.6769 1.3341 1.37271 0.3338 0.5739 1.1337 1.0737 1 0.5646 0.8974 1.7728 1.8347 1 0.50360.8068 1.5920 1.6221 1 0.7396 1.1393 2.2497 2.3883 1 0.3088 0.53361.0521 1.0411 1 0.6048 0.9629 1.9055 1.9526 1 0.3816 0.6382 1.25971.2498 1 0.7480 1.1680 2.3123 2.4163 1 0.7964 1.0918 2.1114 2.7919 10.5280 0.8659 1.7174 1.6980 1 0.3904 0.6532 1.2902 1.2994 1 0.77321.2429 2.4740 2.4864 −1 −0.5306 −0.8886 −1.8440 −2.4780 −1 −0.6806−1.0527 −2.1521 −2.5884 −1 −0.6086 −0.9818 −2.0224 −3.2134 −1 −0.5668−1.0606 −2.2263 −3.7976 −1 −0.5296 −0.7710 −1.5710 −1.5510 −1 −0.6018−0.8906 −1.8137 −1.9054 −1 −0.3928 −0.7334 −1.5506 −2.4400 −1 −0.3510−0.5679 −1.1862 −1.4167 −1 −0.4898 −0.7992 −1.6562 −2.3673 −1 −0.6766−1.0687 −2.1913 −2.9910 −1 −0.5462 −1.1654 −2.4800 −4.9853 −1 −0.4398−0.5996 −1.2163 −1.0113 1 0.7368 1.1777 2.3403 2.4001 1 0.8134 1.29722.5807 2.5924 1 0.7292 1.1146 2.1972 2.4549 1 0.5942 0.9633 1.91151.9163 1 0.6872 1.0695 2.1129 2.2193 1 0.7548 1.1482 2.2629 2.4572 10.7404 1.0506 2.0429 2.4871 1 0.7270 1.1146 2.1982 2.3505 1 0.51460.8564 1.7020 1.7575 1 0.7552 1.2464 2.4911 2.4273 1 0.6542 0.97041.8987 2.2302 1 0.5780 0.9367 1.8576 1.9146 1 0.6580 1.0716 2.13212.1248 1 0.5928 0.9252 1.8236 1.9478 1 0.6276 0.9446 1.8517 2.0843 −1−0.7354 −1.2101 −2.4913 −4.1321 −1 −0.7318 −1.0793 −2.1889 −2.2726 −1−0.8062 −1.2363 −2.5175 −3.3021 −1 −0.6700 −1.1010 −2.2697 −3.1509 −1−0.7018 −1.0655 −2.1714 −2.4599 −1 −0.7784 −1.1794 −2.3989 −2.8846 −1−0.7940 −1.2550 −2.5669 −3.2994 −1 −0.5700 −0.9107 −1.8761 −2.4032 −1−0.6320 −0.9865 −2.0220 −2.4605 −1 −0.7296 −1.0710 −2.1705 −2.2300 −1−0.7084 −1.0685 −2.1752 −2.4564 −1 −0.7504 −1.2075 −2.4779 −4.1647 −1−0.6366 −0.9257 −1.8784 −1.8651 weights w −1, 2 −1.474, −2.976, 6.353,−2.942, 3.433, 1.439, 3.153, −0.337 2.098, 0.063, 0.407, −0.145 −1.369

The invention claimed is:
 1. A method at a computing device, the methodcomprising: deriving weighting constants for a type of chassis of avehicle; obtaining sensor data for the vehicle providing vibrationfrequency and magnitude; calculating an energy for each of a lowfrequency passband and a high frequency passband of a bandpass filterpair; finding an energy ratio based on the energy for the low frequencypassband and the energy for the high frequency passband, wherein theenergy ratio comprises: ${eRatio} = \frac{E_{L}}{E_{L} + E_{H}}$ whereeRatio is the energy ratio; E_(L) is the energy for the low frequencypassband; and E_(H) is the energy for the high frequency passband;applying the weighting constants to each of the energy for the lowfrequency passband, the energy for the high frequency passband and theenergy ratio to calculate a decision variable; and finding that thevehicle is unloaded if the decision variable is below a threshold andfinding that the vehicle is loaded if the decision variable is above athreshold.
 2. The method of claim 1, further comprising repeating thecalculating, finding and applying for a plurality of bandpass filterpairs.
 3. The method of claim 1, wherein the deriving comprisingapplying a machine learning algorithm a plurality of known energies eachof the low frequency passband and the high frequency passband pairs andenergy ratios, along with a known loading status.
 4. The method of claim3, wherein the machine learning algorithm is a minimum-mean squarederror algorithm.
 5. The method of claim 4, wherein each of the pluralityof known energies of the low frequency passband and the high frequencypassband pairs and the energy ratio, along with the known loading statusare represented asy _(n) =w ₀ +w ₁ x _(n1) +w ₂ x _(n2) +. . . +w _(j) x _(nj) and whereinthe plurality of equations may be solved for:y=Xw where $X = \begin{bmatrix}1 & \ldots & x_{1j} \\\vdots & \ddots & \vdots \\1 & \ldots & x_{nj}\end{bmatrix}$ andy=[y ₁ y ₂ . . . y _(n)]^(T) andw=[w ₁ w ₂ . . . w _(j)]^(T).
 6. The method of claim 3, wherein themachine learning algorithm is a support-vector machine algorithm.
 7. Themethod of claim 1, wherein the computing device is a sensor apparatus onthe vehicle.
 8. The method of claim 1, wherein the computing device is aserver remote from the vehicle.
 9. A computing device comprising: aprocessor; and a communications subsystem, wherein the computing deviceis configured to: derive weighting constants for a type of chassis of avehicle; obtain sensor data for the vehicle providing vibrationfrequency and magnitude; calculate an energy for each of a low frequencypassband and a high frequency passband of a bandpass filter pair; findan energy ratio based on the energy for the low frequency passband andthe energy for the high frequency passband, wherein the energy ratiocomprises: ${eRatio} = \frac{E_{L}}{E_{L} + E_{H}}$ where eRatio is theenergy ratio; E_(L) is the energy for the low frequency passband; andE_(H) is the energy for the high frequency passband; apply the weightingconstants to each of the energy for the low frequency passband, theenergy for the high frequency passband and the energy ratio to calculatea decision variable; and find that the vehicle is unloaded if thedecision variable is below a threshold and finding that the vehicle isloaded if the decision variable is above a threshold.
 10. The computingdevice of claim 9, wherein the computing device is further configured torepeat the calculating, finding and applying for a plurality of bandpassfilter pairs.
 11. The computing device of claim 9, wherein the computingdevice is configured to derive by applying a machine learning algorithma plurality of known energies each of the low frequency passband and thehigh frequency passband pairs and the energy ratio, along with a knownloading status.
 12. The computing device of claim 11, wherein themachine learning algorithm is a minimum-mean squared error algorithm.13. The computing device of claim 12, wherein each of the plurality ofknown energies of the low frequency passband and the high frequencypassband pairs and the energy ratio, along with the known loading statusare represented asy _(n) =w ₀ +w ₁ x _(n1) +w ₂ x _(n2) +. . . +w _(j) x _(nj) and whereinthe plurality of equations may be solved for:y=Xw where $X = \begin{bmatrix}1 & \ldots & x_{1j} \\\vdots & \ddots & \vdots \\1 & \ldots & x_{nj}\end{bmatrix}$ andy=[y ₁ y ₂ . . . y _(n)]^(T) andw=[w ₁ w ₂ . . . w _(j)]^(T).
 14. The computing device of claim 11,wherein the machine learning algorithm is a support-vector machinealgorithm.
 15. The computing device of claim 9, wherein the computingdevice is a sensor apparatus on the vehicle.
 16. The computing device ofclaim 9, wherein the computing device is a server remote from thevehicle.
 17. A non-transitory computer readable medium for storinginstruction code, which, when executed by a processor on a computingdevice cause the computing device to: derive weighting constants for atype of chassis of a vehicle; obtain sensor data for the vehicleproviding vibration frequency and magnitude; calculate an energy foreach of a low frequency passband and a high frequency passband of abandpass filter pair; find an energy ratio based on the energy for thelow frequency passband and the energy for the high frequency passband,wherein the energy ratio comprises:${eRatio} = \frac{E_{L}}{E_{L} + E_{H}}$ where eRatio is the energyratio; E_(L) is the energy for the low frequency passband; and E_(H) isthe energy for the high frequency passband; apply the weightingconstants to each of the energy for the low frequency passband, theenergy for the high frequency passband and the energy ratio to calculatea decision variable; and find that the vehicle is unloaded if thedecision variable is below a threshold and finding that the vehicle isloaded if the decision variable is above a threshold.